How does regression analysis help predict a numerical value?

In the context of data mining, regression analysis is a predictive modeling technique used to estimate a specific numerical target attribute for new data.1

It functions by applying mathematical and statistical techniques—such as regression coefficients, curves, or lines—to empirical training data to identify relationships between variables. In its simplest form, linear regression determines a line of best fit that represents the general trend of a dataset. By calculating an equation (typically $y=mx+b$), analysts can model the relationship between an independent variable ($x$) and a dependent variable ($y$).23

Once this equation is established, it serves as a predictive tool: by inputting a known value for an independent variable, the model calculates the corresponding predicted numerical value for the dependent variable. The accuracy of this prediction relies on the strength of the correlation within the data, which can be assessed using tools like Pearson’s correlation coefficient. The least squares method is frequently used to minimize the difference between observed data and the values predicted by the regression equation.3